





import json
from typing import TYPE_CHECKING, List, Optional, Tuple

from tokenizers import pre_tokenizers

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging


if TYPE_CHECKING:
							from transformers.pipelines.conversational import Conversation


UpperCAmelCase_							=				logging.get_logger(__name__)

UpperCAmelCase_							=				{"""vocab_file""": """vocab.json""", """merges_file""": """merges.txt""", """tokenizer_file""": """tokenizer.json"""}

UpperCAmelCase_							=				{
    """tokenizer_file""": {
        """EleutherAI/gpt-neox-20b""": """https://huggingface.co/EleutherAI/gpt-neox-20b/resolve/main/tokenizer.json""",
    },
}

UpperCAmelCase_							=				{
    """gpt-neox-20b""": 2_048,
}
class     UpperCamelCase__					(     snake_case_				):






		'''simple docstring'''






		__a       :       Optional[int]       =	VOCAB_FILES_NAMES
		__a       :       Optional[Any]       =	PRETRAINED_VOCAB_FILES_MAP
		__a       :       int       =	PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
		__a       :       Any       =	["""input_ids""", """attention_mask"""]






		def __init__(							self,   snake_case__=None,   snake_case__=None,   snake_case__=None,   snake_case__="<|endoftext|>",   snake_case__="<|endoftext|>",   snake_case__="<|endoftext|>",   snake_case__=False,   **snake_case__,   )       -> Tuple:






									"""simple docstring"""
									super().__init__(
									    snake_case__,   snake_case__,   tokenizer_file=snake_case__,   unk_token=snake_case__,   bos_token=snake_case__,   eos_token=snake_case__,   add_prefix_space=snake_case__,   **snake_case__,   )

									lowercase_	:   Union[str, Any]  				=     json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()  )
									if pre_tok_state.get("""add_prefix_space""",   snake_case__  ) != add_prefix_space:
																lowercase_	:   List[str]  				=     getattr(snake_case__,   pre_tok_state.pop("""type"""  )  )
																lowercase_	:   Optional[int]  				=     add_prefix_space
																lowercase_	:   Union[str, Any]  				=     pre_tok_class(**snake_case__  )

									lowercase_	:   str  				=     add_prefix_space






		def 						snake_case__				(							self,   snake_case__,   snake_case__ = None  )       -> int:






									"""simple docstring"""
									lowercase_	:   Dict  				=     self._tokenizer.model.save(snake_case__,   name=snake_case__  )
									return tuple(snake_case__  )






		def 						snake_case__				(							self,   snake_case__  )       -> List[Any]:






									"""simple docstring"""
									lowercase_	:   Tuple  				=     []
									for is_user, text in conversation.iter_texts():
																input_ids.extend(self.encode(snake_case__,   add_special_tokens=snake_case__  ) + [self.eos_token_id]  )

									if len(snake_case__  ) > self.model_max_length:
																lowercase_	:   Tuple  				=     input_ids[-self.model_max_length :]
									return input_ids
"""simple docstring"""



import os

import pytest
from attr import dataclass


SCREAMING_SNAKE_CASE__:List[str]									=			"""us-east-1"""  # defaults region






@dataclass
class  snake_case__      :
 _snake_case       :					str
 _snake_case       :					Optional[Any]							  =						"""arn:aws:iam::558105141721:role/sagemaker_execution_role"""
 _snake_case       :					Optional[Any]							  =						{
     """task_name""": """mnli""",
     """per_device_train_batch_size""": 16,
     """per_device_eval_batch_size""": 16,
     """do_train""": True,
     """do_eval""": True,
     """do_predict""": True,
     """output_dir""": """/opt/ml/model""",
     """overwrite_output_dir""": True,
     """max_steps""": 500,
     """save_steps""": 5_500,
 }
 _snake_case       :					List[str]							  =						{**hyperparameters, """max_steps""": 1_000}


 @property
 def 						a__   ( self						):
        if self.framework == "pytorch":
               return [
                   {"Name": "train_runtime", "Regex": r"train_runtime.*=\D*(.*?)$"},
                   {"Name": "eval_accuracy", "Regex": r"eval_accuracy.*=\D*(.*?)$"},
                   {"Name": "eval_loss", "Regex": r"eval_loss.*=\D*(.*?)$"},
               ]
        else:
               return [
                   {"Name": "train_runtime", "Regex": r"train_runtime.*=\D*(.*?)$"},
                   {"Name": "eval_accuracy", "Regex": r"loss.*=\D*(.*?)]?$"},
                   {"Name": "eval_loss", "Regex": r"sparse_categorical_accuracy.*=\D*(.*?)]?$"},
               ]


 @property
 def 						a__   ( self						):
        return F"{self.framework}-transfromers-test"


 @property
 def 						a__   ( self						):
        return F"./tests/sagemaker/scripts/{self.framework}"






 @property
 def 						a__   ( self						):
        if self.framework == "pytorch":
               return "763104351884.dkr.ecr.us-east-1.amazonaws.com/huggingface-pytorch-training:1.7.1-transformers4.6.1-gpu-py36-cu110-ubuntu18.04"
        else:
               return "763104351884.dkr.ecr.us-east-1.amazonaws.com/huggingface-tensorflow-training:2.4.1-transformers4.6.1-gpu-py37-cu110-ubuntu18.04"



@pytest.fixture(scope="class"				)
def 	_lowerCamelCase(	a				):
       __a       =    SageMakerTestEnvironment(framework=request.cls.framework				)
